2020
DOI: 10.1002/rnc.5092
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Abstract: SummaryThis article addresses the problem of maneuvering multiple agents that must visit a number of target sets, while enforcing connectivity constraints and avoiding obstacle as well as interagent collisions. The tool to cope with the problem is a formulation of model predictive control including binary decision variables. In this regard, two mixed‐integer linear programming formulations are presented, considering a trade‐off between optimality and scalability between them. Simulation results are also shown … Show more

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Cited by 23 publications
(40 citation statements)
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“…This problem is addressed with the "big-M" technique, which is implemented with binary variables (Agarwal et al, 2010). We refer the reader to Afonso et al (2020) for details regarding the implementation of this optimization in a linear mixed-integer programming framework.…”
Section: Mas Trajectory Planning With Milpmentioning
confidence: 99%
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“…This problem is addressed with the "big-M" technique, which is implemented with binary variables (Agarwal et al, 2010). We refer the reader to Afonso et al (2020) for details regarding the implementation of this optimization in a linear mixed-integer programming framework.…”
Section: Mas Trajectory Planning With Milpmentioning
confidence: 99%
“…The dominant constraint in terms of computational complexity is the one associated with collision avoidance, with O(T N 2 a n), where n ∈ N is the number of sides of the polytope representing the collision region. Constraints (10) and (11) provide a degree of connectivity robustness which is absent in the formulations presented in Afonso et al (2020), where only 1-connectivity is guaranteed. However, our approach is based on sufficient conditions, whereas the two constraints proposed in Afonso et al (2020) are associated with necessary and necessary sufficient conditions; these constraints scale linearly and exponentially, respectively.…”
Section: Mixed-integer Programming For Robust Connectivitymentioning
confidence: 99%
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